Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "109" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.562977 | 16.395096 | 11.699649 | 12.350500 | 9.217657 | 10.487813 | 0.926951 | 2.349056 | 0.0597 | 0.0367 | 0.0151 | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.652636 | 15.166957 | 13.049923 | 13.609510 | 7.344909 | 8.873288 | 0.519635 | 2.455696 | 0.0620 | 0.0344 | 0.0159 | nan | nan |
| 2460008 | digital_ok | 100.00% | 95.03% | 100.00% | 0.00% | - | - | 14.043323 | 18.551006 | 14.263774 | 14.967222 | 6.601021 | 7.796471 | 4.291776 | 5.608137 | 0.0904 | 0.0385 | 0.0344 | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.456155 | 13.902558 | 11.157248 | 11.703475 | 5.945752 | 7.237270 | 1.172997 | 2.627673 | 0.0585 | 0.0354 | 0.0143 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.58% | 98.75% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3093 | 0.2984 | 0.2314 | nan | nan |
| 2459998 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 8.888857 | 11.721620 | 9.550884 | 9.899216 | 8.038066 | 10.254914 | 0.400792 | 2.029686 | 0.0500 | 0.0321 | 0.0107 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.742378 | 12.801277 | 10.126323 | 10.644197 | 7.770629 | 9.670089 | 1.427947 | 3.369527 | 0.0583 | 0.0341 | 0.0146 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.829615 | 13.776997 | 12.716345 | 13.041087 | 7.329061 | 9.303214 | 0.165906 | 1.242185 | 0.0458 | 0.0330 | 0.0076 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.030250 | 14.003734 | 11.794269 | 12.236419 | 8.117560 | 9.523302 | 0.090224 | 1.211270 | 0.0514 | 0.0365 | 0.0080 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.582700 | 13.594487 | 10.175303 | 10.715619 | 7.840562 | 9.581409 | 0.773749 | 1.739334 | 0.0437 | 0.0337 | 0.0059 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.732482 | 12.688889 | 9.452406 | 9.920170 | 10.245232 | 10.957080 | 0.508156 | 2.402364 | 0.0314 | 0.0299 | 0.0021 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.570970 | 15.825191 | 10.017683 | 10.505258 | 9.241072 | 10.783019 | -0.022185 | 1.016421 | 0.0280 | 0.0332 | 0.0035 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.193069 | 13.062570 | 9.808130 | 10.200391 | 9.150500 | 11.078704 | -0.127326 | 0.792105 | 0.0284 | 0.0339 | 0.0037 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.973677 | 13.207453 | 8.730628 | 9.331312 | 8.071392 | 9.288843 | -0.365846 | 0.495675 | 0.0276 | 0.0293 | 0.0016 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.955217 | 15.465142 | 10.118855 | 10.483080 | 10.853821 | 13.251484 | -0.151177 | 0.729702 | 0.0274 | 0.0293 | 0.0017 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.831044 | 12.968804 | 9.819990 | 10.356207 | 6.427632 | 7.987203 | 0.311290 | 2.022822 | 0.0263 | 0.0302 | 0.0024 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.326254 | 15.891519 | 10.753213 | 11.171855 | 9.448144 | 11.279762 | 5.289769 | 9.730889 | 0.0262 | 0.0293 | 0.0020 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.293807 | 14.383510 | 9.968577 | 10.415000 | 7.281663 | 8.630265 | 0.644563 | 2.044443 | 0.0261 | 0.0287 | 0.0017 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.759539 | 13.805812 | 10.342540 | 10.793222 | 9.441090 | 12.122993 | 1.849621 | 2.911624 | 0.0266 | 0.0293 | 0.0020 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.309598 | 13.149898 | 9.376724 | 9.686304 | 9.294923 | 11.135537 | 2.500249 | 6.154492 | 0.0276 | 0.0303 | 0.0020 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 8.933842 | 11.039730 | 8.390020 | 8.715479 | 4.562910 | 5.282956 | 2.372034 | 3.207024 | 0.0281 | 0.0261 | 0.0018 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.804343 | 12.433442 | 10.539915 | 10.847937 | 10.508809 | 12.382365 | -0.000581 | 1.151911 | 0.0283 | 0.0260 | 0.0020 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.626946 | 11.982574 | 9.476532 | 9.920581 | 9.118643 | 10.842015 | 5.134341 | 5.420350 | 0.0288 | 0.0264 | 0.0020 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.995669 | 12.526124 | 8.773113 | 9.275485 | 9.005878 | 10.138899 | 0.312308 | 1.327067 | 0.0300 | 0.0262 | 0.0019 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.101255 | 12.721996 | 9.529859 | 9.984495 | 9.402739 | 10.986861 | -0.263714 | 1.125960 | 0.0275 | 0.0257 | 0.0017 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.424825 | 13.446329 | 9.361321 | 9.848216 | 9.299212 | 11.336504 | 0.579950 | 2.100385 | 0.0287 | 0.0262 | 0.0021 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 3.761148 | 12.948232 | 7.586861 | 10.251845 | 3.771131 | 10.875317 | -0.541928 | 1.301880 | 0.5082 | 0.0300 | 0.3524 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 16.395096 | 12.562977 | 16.395096 | 11.699649 | 12.350500 | 9.217657 | 10.487813 | 0.926951 | 2.349056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 15.166957 | 11.652636 | 15.166957 | 13.049923 | 13.609510 | 7.344909 | 8.873288 | 0.519635 | 2.455696 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 18.551006 | 18.551006 | 14.043323 | 14.967222 | 14.263774 | 7.796471 | 6.601021 | 5.608137 | 4.291776 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.902558 | 10.456155 | 13.902558 | 11.157248 | 11.703475 | 5.945752 | 7.237270 | 1.172997 | 2.627673 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 11.721620 | 8.888857 | 11.721620 | 9.550884 | 9.899216 | 8.038066 | 10.254914 | 0.400792 | 2.029686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.801277 | 9.742378 | 12.801277 | 10.126323 | 10.644197 | 7.770629 | 9.670089 | 1.427947 | 3.369527 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.776997 | 10.829615 | 13.776997 | 12.716345 | 13.041087 | 7.329061 | 9.303214 | 0.165906 | 1.242185 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 14.003734 | 11.030250 | 14.003734 | 11.794269 | 12.236419 | 8.117560 | 9.523302 | 0.090224 | 1.211270 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.594487 | 10.582700 | 13.594487 | 10.175303 | 10.715619 | 7.840562 | 9.581409 | 0.773749 | 1.739334 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.688889 | 11.732482 | 12.688889 | 9.452406 | 9.920170 | 10.245232 | 10.957080 | 0.508156 | 2.402364 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 15.825191 | 12.570970 | 15.825191 | 10.017683 | 10.505258 | 9.241072 | 10.783019 | -0.022185 | 1.016421 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.062570 | 13.062570 | 10.193069 | 10.200391 | 9.808130 | 11.078704 | 9.150500 | 0.792105 | -0.127326 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.207453 | 13.207453 | 9.973677 | 9.331312 | 8.730628 | 9.288843 | 8.071392 | 0.495675 | -0.365846 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 15.465142 | 15.465142 | 11.955217 | 10.483080 | 10.118855 | 13.251484 | 10.853821 | 0.729702 | -0.151177 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.968804 | 9.831044 | 12.968804 | 9.819990 | 10.356207 | 6.427632 | 7.987203 | 0.311290 | 2.022822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 15.891519 | 15.891519 | 12.326254 | 11.171855 | 10.753213 | 11.279762 | 9.448144 | 9.730889 | 5.289769 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 14.383510 | 14.383510 | 11.293807 | 10.415000 | 9.968577 | 8.630265 | 7.281663 | 2.044443 | 0.644563 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.805812 | 10.759539 | 13.805812 | 10.342540 | 10.793222 | 9.441090 | 12.122993 | 1.849621 | 2.911624 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.149898 | 10.309598 | 13.149898 | 9.376724 | 9.686304 | 9.294923 | 11.135537 | 2.500249 | 6.154492 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 11.039730 | 8.933842 | 11.039730 | 8.390020 | 8.715479 | 4.562910 | 5.282956 | 2.372034 | 3.207024 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.433442 | 12.433442 | 9.804343 | 10.847937 | 10.539915 | 12.382365 | 10.508809 | 1.151911 | -0.000581 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 11.982574 | 11.982574 | 9.626946 | 9.920581 | 9.476532 | 10.842015 | 9.118643 | 5.420350 | 5.134341 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.526124 | 9.995669 | 12.526124 | 8.773113 | 9.275485 | 9.005878 | 10.138899 | 0.312308 | 1.327067 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.721996 | 12.721996 | 10.101255 | 9.984495 | 9.529859 | 10.986861 | 9.402739 | 1.125960 | -0.263714 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.446329 | 10.424825 | 13.446329 | 9.361321 | 9.848216 | 9.299212 | 11.336504 | 0.579950 | 2.100385 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 12.948232 | 12.948232 | 3.761148 | 10.251845 | 7.586861 | 10.875317 | 3.771131 | 1.301880 | -0.541928 |